\section{Related work}
\label{sec:RelatedWork}

Most machine learning algorithms used in the literature, including evolutionary algorithms on wireless ad hoc networks focus on network optimization problem, such as increasing energy efficiency and network lifetime.
Ranganathan et al.~\cite{Ranganathan-2006-heuristics}, presents a preliminary attempt towards a systematic approach using evolutionary algorithms and reverse engineering, to provide improvements in network lifetime, power efficiency and routing.
Chaudhry et al.~\cite{Chaudhry-2006-Optimal} proposes a network optimization approach by using evolutionary infrastructure based on NEAT technique. They try to maximize the coverage area, to minimize the energy cost, and to maximize the percentage of nodes that satisfy k-connectivity via this technique. This approach has similarities with our approach, since they also try to optimize networks with wireless sensor nodes, but the goal of our approach is to solve the intrusion problem for network security and data integrity, whereas we also try to increase network performance to optimal levels, that are acceptable for network designers.

Knoester et al.~\cite{Knoester-2010-mobile} presents a study of evolution of distributed behavior, specifically the control of agents in a mobile ad hoc network, using three different neuroevolution strategies. They compare the performance of three different neuroevolutionary systems: a direct encoding, an indirect encoding, and an indirect encoding that supports heterogeneity. They found that although direct and indirect encodings tend to perform similarly, the strategies employed by indirect encodings tend to favor stable, cohesive groups, while the direct encoding versions appeared more stochastic in nature. Knoester et al.~\cite{Knoester-2011-Neuroevolution}, uses neuroevolution to study a generic coverage-based problem, where agents in the network aim to maximize the area covered by the largest connected component of the network and shows that neuroevolution may be a viable strategy for discovering controllers for self-organizing multi-agent systems.


